Inconsistency of a model aimed at predicting bacteremia in hospitalized patients

Mozes, Benjamin; Milatiner, David; Block, Colin; Blumstein, Zvia; Halkin, Hillel

doi:10.1016/0895-4356(93)90171-v

Cited by 34 publications

(19 citation statements)

References 8 publications

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“…They have not entered wide clinical use [21]. A major problem observed with previous models was their degradation when applied to populations different than those used for model development [20,22,23]. Specifically, when transferred to other hospitals, the models could not define a group of patients with a risk low enough as not to necessitate the performance of blood cultures.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of Bacteremia Using TREAT, a Computerized Decision-Support System

Paul¹,

Andreassen²,

Nielsen³

et al. 2006

Clinical Infectious Diseases

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Section: Discussionmentioning

confidence: 99%

“…Several clinical models to predict bacteremia have been developed [2,5,[17][18][19][20]. They have not entered wide clinical use [21].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Bacteremia Using TREAT, a Computerized Decision-Support System

Paul¹,

Andreassen²,

Nielsen³

et al. 2006

Clinical Infectious Diseases

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“…Mylotte et al attempted to validate Bates' model in a different institution with similar patients and found a slight degradation in performance, with a 3% misclassification rate for low-risk patients (88). Misclassification rates for low-risk groups in models developed by other investigators ranged from 4.6 to 5.1% (80,85,148). As several of the authors of those studies acknowledged, such results may be insufficient for reducing blood culture testing.…”

Section: Clinical Prediction Modelsmentioning

confidence: 99%

“…For a prediction model to be maximally useful, it should be applicable to other settings (148). Researchers have developed or studied models for predicting bacteremia in hospitalized adults (12,65,72,80,85,88,160) and adult patients seen in emergency departments (40,120,159). Similar efforts have been explored in pediatric populations, although the motivation for many of these models is targeted primarily at decisionmaking regarding hospital admission versus outpatient management and initiation of empirical therapy and not whether or not to obtain blood cultures (3,4,8,61,70,99).…”

Section: Clinical Prediction Modelsmentioning

confidence: 99%

Updated Review of Blood Culture Contamination

2006

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SUMMARY Blood culture contamination represents an ongoing source of frustration for clinicians and microbiologists alike. Ambiguous culture results often lead to diagnostic uncertainty in clinical management and are associated with increased health care costs due to unnecessary treatment and testing. A variety of strategies have been investigated and employed to decrease contamination rates. In addition, numerous approaches to increase our ability to distinguish between clinically significant bacteremia and contamination have been explored. In recent years, there has been an increase in the application of computer-based tools to support infection control activities as well as provide clinical decision support related to the management of infectious diseases. Finally, new approaches for estimating bacteremia risk which have the potential to decrease unnecessary blood culture utilization have been developed and evaluated. In this review, we provide an overview of blood culture contamination and describe the potential utility of a variety of approaches to improve both detection and prevention. While it is clear that progress is being made, fundamental challenges remain.

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“…Efforts to improve blood culture practice have focused on developing clinical prediction rules to identify patients at risk for bacteremia. Several such models have been developed [1,6], but none have been accepted into general use.…”

Section: Introductionmentioning

confidence: 99%